In a remote diagnosis method for diagnosing the abnormality of a target system (air conditioning system, water system, electric power plant, etc.) by remotely monitoring process data of the target system such as a sensor measured value and a control instruction value, it is a general technique to keep the score of diagnosis result as abnormality level to issue an alarm to an observer when the score exceeds a warning value.
When the warning value is set low not to overlook abnormality, the problem of “false detection” frequently occurs, which is because an abnormality alarm is easily issued when the target is normal. On the other hand, when the warning value is set high, the problem of “overlooking” frequently occurs, which is because the target which is actually abnormal is easily judged to be normal.
As a prior art to avoid such problems, it is known to issue a warning when the target is judged to be abnormal a plurality of times within a set period, or to set the warning value automatically.
However, the former technique has a problem that the performance of the diagnostic system depends on the user since the period and judgment frequency should be determined by trial and error.
On the other hand, in the latter technique, warning value parameters can be set automatically, but there is a problem that solution cannot be obtained when the performance of the abnormality diagnosis is not sufficient.
Both of the techniques has an object to set optimum operational parameters for univariate input (diagnosis result or sensor measured value), which leads to a problem that the number operational parameters linearly increases as the number of sensors and the number of diagnosis results increase.